Infrared Small Target Detection Using Local and Nonlocal Spatial Information

Existing infrared (IR) small target detection methods are divided into local priors-based and nonlocal priors-based ones. However, due to heterogeneous structures in IR images, using either local or nonlocal information is always suboptimal, which causes detection performance to be unstable and unrobust. To solve the issue, a comprehensive method, exploiting both local and nonlocal priors, is proposed. The proposed method framework includes dual-window local contrast method (DW-LCM) and multiscale-window IR patch-image (MW-IPI). In the first stage, DW-LCM designs dual-window to compensate for the shortcomings of local priors-based methods, which easily mistake some isolated weak-signal targets. In the second stage, MW-IPI utilizes several small windows with various sizes, which can not only decrease the redundant information generated by sliding windows, but also extract more discriminative information to prevent some pixels in the strong-border edge from being falsely detected. Then, multiplication pooling operation is employed to enhance the target separation and suppress the background clutter simultaneously. Experimental results using five real IR datasets with various scenes reveal the effectiveness and robustness of the proposed method.